The present invention generally relates to a device and method for improving the profile and flatness of a rolled article, and more particularly to a device and method that can calculate the cross-sectional thickness profile of the rolled article based on the machine parameters and provide instructions to control the article's profile and flatness accordingly. The invention even more particularly relates to predictive measurement and optional corrective actions by a controller on rolled metal plate, strip or sheet articles.
Metal and non-metal articles in plate, strip or sheet form may be produced by rolling. The use of rolling equipment, especially rolling mills, is particularly prevalent in the production of metal articles. In order to achieve a high level of dimensional quality in the rolling of metal plate, strip, or sheet (hereafter collectively referred to as “strip”), the variation of the cross-sectional strip thickness, also referred to as the thickness profile or profile, must lie within acceptable limits. One common measurement of the strip thickness profile is the strip “crown”, which is defined as the difference between the thickness at the strip center and an average edge thickness. In addition to the strip thickness profile, a second important dimensional quality criterion of the rolled metal strip is the related variation in length, also referred to as the flatness or shape.
Intense global competition and the difficulties associated with rolling increasingly thinner, higher quality metal strip place demands upon metal producers to commission innovative profile and flatness controlling technologies. Measures undertaken by metals manufacturers to meet the strip profile and flatness requirements typically include the employment of on-line controls systems that operate rolling mill actuators for the purpose of optimizing the thickness profile and/or flatness during rolling, the optimization of suitable ground profiles onto the rolls that make possible the desired profile and flatness, and the optimization of strip thickness reduction schedules that facilitate the desired profile and flatness. Additional measures taken by the manufacturers of the rolling mill equipment and of supplemental profile and flatness control mechanisms respectively may include optimum design of the rolling mills to achieve desired profile and flatness, and design of effective supplemental hardware mechanisms capable of attaining desired strip profile and flatness.
In order to produce a desired strip profile and flatness, the aforementioned measures taken by metal producers, rolling mill manufacturers and suppliers of supplemental profile and flatness control mechanisms require analytical tools to predict and control the profile and flatness for a specific mill configuration, mechanical control mechanism(s), and rolled material properties. Because of the complexity in modeling rolling mills, particularly those having cluster-type roll configurations, conventional systems to predict and control strip profile and flatness have employed analytical methods with various types of simplifying assumptions.
The conventional analytical techniques incorporated into systems to predict and control the profile and flatness of rolled metals can be categorized into five broad methods: (1) single-beam on elastic foundation method; (2) influence coefficient/point match method; (3) transport matrix method; (4) pattern recognition/heuristic method and (5) large-scale finite element method. Each of these conventional methods used to predict and/or control rolled metal profile and flatness are deficient due to one or more general shortcomings.
The first general shortcoming is limited applicability. Because of the inherent complexity of typical rolling mills (especially cluster-type rolling stand configurations), few of the conventional analytical methods readily encompass the details necessary to attain an accurate result, while a more simplistic method, such as the single beam on elastic foundation method, is not well-suited to the intricacies of cluster-type and related modern rolling stand configurations. Of the methods that have been devised for use in cluster-type mills, such as the influence coefficient/point match and transport matrix methods, excessively complex models with limited transferability have arisen. For this reason, the prevalence of non-physics based pattern recognition/heuristic models is greater in predicting and controlling profile and flatness in cluster-type rolling mills, although they suffer from other shortcomings, as discussed below.
The second general shortcoming is excessive computation time. The most widely employed method, the influence coefficient/point match method, requires an iterative computational procedure in conjunction with convergence (loop terminating) criteria to obtain a result. Due to the number of iterations and associated computation time, the influence coefficient/point match method is not directly suitable for on-line and related real-time prediction and control in rolling mills. While the transport matrix method has been used on-line for vertical-stack (non cluster type) rolling mills, it is also not suitably fast enough for mills having relatively large numbers of rolls, such as the 20-roll Sendzimir cluster-type mill. Large-scale finite element methods require the most computation time of any conventional method. Even for off-line studies, wherein execution time is not critical, the finite element method's use is questionable because of the convergence issues and lengthy computation time associated with contact-type structural analyses.
The third general shortcoming is insufficient accuracy. The single beam on elastic foundation method is inaccurate in all instances because it neglects shear deformation of the work rolls and considers deflection of the backup rolls (shear, bending, and flattening) as a constant elastic foundation. The influence coefficient/point match method and transport matrix method suffer inaccuracy because the strip profile is predicted in a piecewise continuous (“connect-the-dots”) manner, with accuracy conditional upon a relatively large number of closely-spaced nodes. As node count is increased to improve accuracy, computation time and speed are adversely affected. In addition, since the transport matrix method employs a model of discretely separated nodal springs instead of a continuous elastic foundation that is mathematically integrated, accuracy is sacrificed, particularly in the vicinity of component ends where accuracy is most important.
The fourth general shortcoming is the prerequisite of training the profile and flatness prediction and control system with large amounts of data collected from the rolling operation. Since pattern recognition/heuristic models are non-physics based, they exhibit deficiencies in both trend and accuracy in the absence of training with actual data. Such required data may not be available prior to commissioning a strip profile and flatness control system, particularly for newly-started rolling mills.
The fifth general shortcoming is the inability of any of the conventional methods to predict the dynamic deflection behavior of the rolling mills. While not traditionally considered by methods that statically predict strip profile and flatness, the ability to predict adverse dynamic characteristics of rolling mills can prevent severe problems in dimensional quality in addition to costly mill equipment damage. With the exception of large-scale commercial finite element methods, none of the conventional methods previously described are able to predict and control dynamic deflection of rolling mill stands.
While the conventional approaches are currently being employed, their effectiveness is limited by one or more of the aforementioned problems and disadvantages. Thus, what is needed is a profile and flatness prediction and control system and method that can operate accurately to attain the desired strip profile and flatness for both cluster-type and non cluster-type rolling mills. What is further needed is such a system that can operate rapidly in real-time (i.e., on-line) strip-producing conditions.